
import numpy as np # linear algebra....
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)....
from matplotlib import pyplot as plt #Visualization of the data....
from mpl_toolkits import mplot3d
import missingno as mn
import seaborn as sns
import warnings
import plotly.express as px
warnings.filterwarnings("ignore")
pd.set_option("display.max_columns",None)
pd.set_option("display.max_rows",None)


# Read the data frame
df = pd.read_csv(r"C:\Users\LALIT YADAV\Downloads\loans_full_schema.csv", delimiter=',')
df.head(10)


print("The shape of the Training Dataset is:",df.shape)

The shape of the Training Dataset is: (10000, 55)


print("The Dimensions of the Training Dataset is:",df.ndim)

The Dimensions of the Training Dataset is: 2


print("Column names in the Training Dataset are:\n",df.columns)

Column names in the Training Dataset are:
 Index(['emp_title', 'emp_length', 'state', 'homeownership', 'annual_income',
       'verified_income', 'debt_to_income', 'annual_income_joint',
       'verification_income_joint', 'debt_to_income_joint', 'delinq_2y',
       'months_since_last_delinq', 'earliest_credit_line',
       'inquiries_last_12m', 'total_credit_lines', 'open_credit_lines',
       'total_credit_limit', 'total_credit_utilized',
       'num_collections_last_12m', 'num_historical_failed_to_pay',
       'months_since_90d_late', 'current_accounts_delinq',
       'total_collection_amount_ever', 'current_installment_accounts',
       'accounts_opened_24m', 'months_since_last_credit_inquiry',
       'num_satisfactory_accounts', 'num_accounts_120d_past_due',
       'num_accounts_30d_past_due', 'num_active_debit_accounts',
       'total_debit_limit', 'num_total_cc_accounts', 'num_open_cc_accounts',
       'num_cc_carrying_balance', 'num_mort_accounts',
       'account_never_delinq_percent', 'tax_liens', 'public_record_bankrupt',
       'loan_purpose', 'application_type', 'loan_amount', 'term',
       'interest_rate', 'installment', 'grade', 'sub_grade', 'issue_month',
       'loan_status', 'initial_listing_status', 'disbursement_method',
       'balance', 'paid_total', 'paid_principal', 'paid_interest',
       'paid_late_fees'],
      dtype='object')


#check for unique values
df.nunique()

emp_title                           4741
emp_length                            11
state                                 50
homeownership                          3
annual_income                       1463
verified_income                        3
debt_to_income                      3673
annual_income_joint                  596
verification_income_joint              3
debt_to_income_joint                1189
delinq_2y                             12
months_since_last_delinq              97
earliest_credit_line                  53
inquiries_last_12m                    26
total_credit_lines                    78
open_credit_lines                     45
total_credit_limit                  9119
total_credit_utilized               9497
num_collections_last_12m               4
num_historical_failed_to_pay           9
months_since_90d_late                106
current_accounts_delinq                2
total_collection_amount_ever         896
current_installment_accounts          30
accounts_opened_24m                   26
months_since_last_credit_inquiry      25
num_satisfactory_accounts             45
num_accounts_120d_past_due             1
num_accounts_30d_past_due              2
num_active_debit_accounts             25
total_debit_limit                   1222
num_total_cc_accounts                 56
num_open_cc_accounts                  40
num_cc_carrying_balance               30
num_mort_accounts                     15
account_never_delinq_percent         282
tax_liens                              9
public_record_bankrupt                 4
loan_purpose                          12
application_type                       2
loan_amount                          612
term                                   2
interest_rate                         58
installment                         3540
grade                                  7
sub_grade                             32
issue_month                            3
loan_status                            6
initial_listing_status                 2
disbursement_method                    2
balance                             5741
paid_total                          7475
paid_principal                      5765
paid_interest                       7422
paid_late_fees                        29
dtype: int64


df['Id']=df.index


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000 entries, 0 to 9999
Data columns (total 56 columns):
 #   Column                            Non-Null Count  Dtype  
---  ------                            --------------  -----  
 0   emp_title                         9167 non-null   object 
 1   emp_length                        9183 non-null   float64
 2   state                             10000 non-null  object 
 3   homeownership                     10000 non-null  object 
 4   annual_income                     10000 non-null  float64
 5   verified_income                   10000 non-null  object 
 6   debt_to_income                    9976 non-null   float64
 7   annual_income_joint               1495 non-null   float64
 8   verification_income_joint         1455 non-null   object 
 9   debt_to_income_joint              1495 non-null   float64
 10  delinq_2y                         10000 non-null  int64  
 11  months_since_last_delinq          4342 non-null   float64
 12  earliest_credit_line              10000 non-null  int64  
 13  inquiries_last_12m                10000 non-null  int64  
 14  total_credit_lines                10000 non-null  int64  
 15  open_credit_lines                 10000 non-null  int64  
 16  total_credit_limit                10000 non-null  int64  
 17  total_credit_utilized             10000 non-null  int64  
 18  num_collections_last_12m          10000 non-null  int64  
 19  num_historical_failed_to_pay      10000 non-null  int64  
 20  months_since_90d_late             2285 non-null   float64
 21  current_accounts_delinq           10000 non-null  int64  
 22  total_collection_amount_ever      10000 non-null  int64  
 23  current_installment_accounts      10000 non-null  int64  
 24  accounts_opened_24m               10000 non-null  int64  
 25  months_since_last_credit_inquiry  8729 non-null   float64
 26  num_satisfactory_accounts         10000 non-null  int64  
 27  num_accounts_120d_past_due        9682 non-null   float64
 28  num_accounts_30d_past_due         10000 non-null  int64  
 29  num_active_debit_accounts         10000 non-null  int64  
 30  total_debit_limit                 10000 non-null  int64  
 31  num_total_cc_accounts             10000 non-null  int64  
 32  num_open_cc_accounts              10000 non-null  int64  
 33  num_cc_carrying_balance           10000 non-null  int64  
 34  num_mort_accounts                 10000 non-null  int64  
 35  account_never_delinq_percent      10000 non-null  float64
 36  tax_liens                         10000 non-null  int64  
 37  public_record_bankrupt            10000 non-null  int64  
 38  loan_purpose                      10000 non-null  object 
 39  application_type                  10000 non-null  object 
 40  loan_amount                       10000 non-null  int64  
 41  term                              10000 non-null  int64  
 42  interest_rate                     10000 non-null  float64
 43  installment                       10000 non-null  float64
 44  grade                             10000 non-null  object 
 45  sub_grade                         10000 non-null  object 
 46  issue_month                       10000 non-null  object 
 47  loan_status                       10000 non-null  object 
 48  initial_listing_status            10000 non-null  object 
 49  disbursement_method               10000 non-null  object 
 50  balance                           10000 non-null  float64
 51  paid_total                        10000 non-null  float64
 52  paid_principal                    10000 non-null  float64
 53  paid_interest                     10000 non-null  float64
 54  paid_late_fees                    10000 non-null  float64
 55  Id                                10000 non-null  int64  
dtypes: float64(17), int64(26), object(13)
memory usage: 4.3+ MB


df.describe()


#Visualizing the missing values
mn.matrix(df,color=(0,0,0))

<AxesSubplot:>


## Here we will check the percentage of nan values present in each feature
## 1 -step make the list of features which has missing values
features_with_na=[features for features in df.columns if df[features].isnull().sum()>1]
## 2- step print the feature name and the percentage of missing values

for feature in features_with_na:
    print(feature, np.round(df[feature].isnull().mean(), 4),'% missing values.\n')

emp_title 0.0833 % missing values.

emp_length 0.0817 % missing values.

debt_to_income 0.0024 % missing values.

annual_income_joint 0.8505 % missing values.

verification_income_joint 0.8545 % missing values.

debt_to_income_joint 0.8505 % missing values.

months_since_last_delinq 0.5658 % missing values.

months_since_90d_late 0.7715 % missing values.

months_since_last_credit_inquiry 0.1271 % missing values.

num_accounts_120d_past_due 0.0318 % missing values.


#scatterplot
sns.set()
cols = ['annual_income','verified_income', 'debt_to_income','total_credit_lines', 'open_credit_lines','total_credit_limit',
        'total_credit_utilized','num_collections_last_12m', 'num_historical_failed_to_pay','total_debit_limit','loan_amount',
        'interest_rate']
sns.pairplot(df[cols], size = 2.5)
plt.show();


fig = px.scatter(df, x="open_credit_lines", y="total_credit_lines", color="application_type")
fig.show()


bar = df.groupby(["application_type","loan_purpose"])['loan_amount'].sum().reset_index()
bar.columns = ["application_type","loan_purpose",'loan_amount']
px.bar(data_frame=bar, x = 'loan_purpose', y = 'loan_amount',color="application_type",title="Loan Amount by Loan Type, Application Type")


bar = df.groupby(["state"])['loan_amount'].sum().reset_index()
bar=bar.sort_values("loan_amount")
bar.columns = ["state",'loan_amount']
px.bar(data_frame=bar, x = 'loan_amount', y = 'state',title="Loan Amount by Loan Type, Application Type",width=800, height=1000)


p=df.groupby(['homeownership'])['loan_amount'].sum().reset_index()
fig = px.pie(p, values='loan_amount', names='homeownership',title="Loan Amount by Homeownership")
fig.show()


d=df.groupby(['loan_status'])['total_credit_lines'].sum().reset_index()
fig = px.pie(d, values='total_credit_lines', names='loan_status',title="total_credit_lines by loan_status")
fig.show()


print("Id of Person {}".format(len(df.Id)))

Id of Person 10000


# list of numerical variables............
numerical_features = [feature for feature in df.columns if df[feature].dtypes != 'O']

print('Number of numerical variables: ', len(numerical_features))
print('\n')
# visualise the numerical variables........
df[numerical_features].head()

Number of numerical variables:  43


#Basic statistic on saleprice..
df['interest_rate'].describe()

count    10000.000000
mean        12.427524
std          5.001105
min          5.310000
25%          9.430000
50%         11.980000
75%         15.050000
max         30.940000
Name: interest_rate, dtype: float64


categorical_features=[feature for feature in df.columns if df[feature].dtypes=='O']
categorical_features

['emp_title',
 'state',
 'homeownership',
 'verified_income',
 'verification_income_joint',
 'loan_purpose',
 'application_type',
 'grade',
 'sub_grade',
 'issue_month',
 'loan_status',
 'initial_listing_status',
 'disbursement_method']


# Check number of categories
for feature in categorical_features:
    print('The feature is {} and number of categories are {}.'.format(feature,len(df[feature].unique())))
    print('\n')

The feature is emp_title and number of categories are 4742.


The feature is state and number of categories are 50.


The feature is homeownership and number of categories are 3.


The feature is verified_income and number of categories are 3.


The feature is verification_income_joint and number of categories are 4.


The feature is loan_purpose and number of categories are 12.


The feature is application_type and number of categories are 2.


The feature is grade and number of categories are 7.


The feature is sub_grade and number of categories are 32.


The feature is issue_month and number of categories are 3.


The feature is loan_status and number of categories are 6.


The feature is initial_listing_status and number of categories are 2.


The feature is disbursement_method and number of categories are 2.


#PERCENTAGE OF THE MISSING VALUES IN THE DATAFRAME..
def missing_data(df):
    total = df.isnull().sum().sort_values(ascending = False)
    Percentage = (df.isnull().sum()/df.isnull().count()*100).sort_values(ascending = False)
    Dtypes =df.dtypes
    return pd.concat([total, Percentage,Dtypes], axis=1, keys=['Total', 'Percentage','Dtypes'])
missing_data(df)


df = df.drop(['verification_income_joint','debt_to_income_joint','annual_income_joint',
         'months_since_90d_late'], axis=1)


# Dealing with Numerical missing values
df['months_since_last_delinq']=df['months_since_last_delinq'].fillna(df['months_since_last_delinq'].median())
df['months_since_last_credit_inquiry']=df['months_since_last_credit_inquiry'].fillna(df['months_since_last_credit_inquiry'].median())
df['emp_length']=df['emp_length'].fillna(df['emp_length'].median())
df['num_accounts_120d_past_due']=df['num_accounts_120d_past_due'].fillna(df['num_accounts_120d_past_due'].median())
df['debt_to_income']=df['debt_to_income'].fillna(df['debt_to_income'].median())


#Dealing with categorical Missing values
df['emp_title']=df['emp_title'].fillna(df['emp_title'].mode()[0])


#PERCENTAGE OF THE MISSING VALUES IN THE DATAFRAME..
def missing_data(df):
    total = df.isnull().sum().sort_values(ascending = False)
    Percentage = (df.isnull().sum()/df.isnull().count()*100).sort_values(ascending = False)
    Dtypes =df.dtypes
    return pd.concat([total, Percentage,Dtypes], axis=1, keys=['Total', 'Percentage','Dtypes'])
missing_data(df)


df.head(10)


import seaborn as sns
plt.figure(figsize=(35,20))
sns.heatmap(df.corr(),annot=True,cmap="twilight_shifted_r",linewidth = 1,linecolor = "white")

<AxesSubplot:>


#Encoding Categorical Variables
from sklearn.preprocessing import LabelEncoder
labelencoder=LabelEncoder()
for col in df.columns:
    df[col] = labelencoder.fit_transform(df[col])


df.head()


print("The shape of the Training Dataset is:",df.shape)

The shape of the Training Dataset is: (10000, 52)


#Now creating dummies
df=pd.get_dummies(df, columns=['emp_title',
 'state',
 'homeownership',
 'verified_income',
 'loan_purpose',
 'application_type',
 'grade',
 'sub_grade',
 'issue_month',
 'loan_status',
 'initial_listing_status',
 'disbursement_method'],drop_first=True)


df.head()


print("Column names in the Final Dataset are:\n",df.columns)

Column names in the Final Dataset are:
 Index(['emp_length', 'annual_income', 'debt_to_income', 'delinq_2y',
       'months_since_last_delinq', 'earliest_credit_line',
       'inquiries_last_12m', 'total_credit_lines', 'open_credit_lines',
       'total_credit_limit',
       ...
       'sub_grade_31', 'issue_month_1', 'issue_month_2', 'loan_status_1',
       'loan_status_2', 'loan_status_3', 'loan_status_4', 'loan_status_5',
       'initial_listing_status_1', 'disbursement_method_1'],
      dtype='object', length=4891)


#PERCENTAGE OF THE MISSING VALUES IN THE DATAFRAME..
def missing_data(df):
    total = df.isnull().sum().sort_values(ascending = False)
    Percentage = (df.isnull().sum()/df.isnull().count()*100).sort_values(ascending = False)
    Dtypes =df.dtypes
    return pd.concat([total,Percentage,Dtypes], axis=1, keys=['Total','Percentage','Dtypes'])
missing_data(df).head()


df.shape

(10000, 4891)


X=df.drop(['interest_rate'],axis=1)
y=df['interest_rate']


X.shape

(10000, 4890)


y.shape

(10000,)


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=324)


# Applying PCA
from sklearn.decomposition import PCA
pca = PCA(n_components = 25)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)
explained_variance = pca.explained_variance_ratio_
print(explained_variance)
print(len(explained_variance))

[4.00238875e-01 2.12149484e-01 2.05542870e-01 8.16175210e-02
 5.93572057e-02 1.99798343e-02 1.36128261e-02 2.43615642e-03
 1.85584168e-03 1.41264094e-03 1.07431052e-03 5.95435976e-04
 7.62310212e-05 3.71777493e-05 5.12926845e-06 4.22280161e-06
 1.34982237e-06 7.97262227e-07 6.94457648e-07 4.16961539e-07
 2.76841318e-07 1.76135065e-07 1.45795579e-07 7.62048826e-08
 6.78409338e-08]
25


#Fit Decision_tree
from sklearn.ensemble import RandomForestRegressor
forest = RandomForestRegressor()
forest.fit(X_train, y_train)

RandomForestRegressor()


#Fit Bagging_Regressor..........
from sklearn.ensemble import BaggingRegressor
Bagging = BaggingRegressor()
Bagging.fit(X_train, y_train)

BaggingRegressor()


FR_y_prediction = forest.predict(X_test)
FR_y_prediction = pd.Series(FR_y_prediction)
BG_y_prediction = Bagging.predict(X_test)
BG_y_prediction = pd.Series(BG_y_prediction)


y_test.describe()

count    3300.000000
mean       20.161212
std        11.848419
min         0.000000
25%        11.000000
50%        20.000000
75%        28.000000
max        56.000000
Name: interest_rate, dtype: float64


FR_y_prediction.describe()

count    3300.000000
mean       20.112755
std        10.138546
min         1.260000
25%        12.710000
50%        19.075000
75%        26.525000
max        54.410000
dtype: float64


BG_y_prediction.describe()

count    3300.000000
mean       20.107758
std        10.266678
min         0.800000
25%        12.700000
50%        19.200000
75%        26.600000
max        54.000000
dtype: float64


from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
from math import sqrt

#RANDOM FOREST

FR_RMSE = sqrt(mean_squared_error(y_true = y_test, y_pred = FR_y_prediction))
FR_MAE = np.mean(np.absolute(FR_y_prediction - y_test))
FR_MSE=np.mean((FR_y_prediction - y_test) ** 2)
FR_R2_score = r2_score(y_test,FR_y_prediction)

print("Random Forest Mean absolute error: %.2f" % FR_MAE)
print("Random Forest Residual sum of squares (MSE): %.2f" % FR_MSE)
print("Random Forest Root mean squares (MSE): %.2f" % FR_RMSE)
print("Random Forest R2-score: %.2f" % FR_R2_score)

#Bagging

BG_RMSE = sqrt(mean_squared_error(y_true = y_test, y_pred = BG_y_prediction))
BG_MAE = np.mean(np.absolute(BG_y_prediction - y_test))
BG_MSE=np.mean((BG_y_prediction - y_test) ** 2)
BG_R2_score = r2_score(y_test,BG_y_prediction)

print("Bagging Mean absolute error: %.2f" % BG_MAE)
print("Bagging Residual sum of squares (MSE): %.2f" % BG_MSE)
print("Bagging Root mean squares (MSE): %.2f" % BG_RMSE)
print("Bagging R2-score: %.2f" % BG_R2_score)

Random Forest Mean absolute error: 12.54
Random Forest Residual sum of squares (MSE): 240.18
Random Forest Root mean squares (MSE): 4.40
Random Forest R2-score: 0.86
Bagging Mean absolute error: 12.57
Bagging Residual sum of squares (MSE): 242.38
Bagging Root mean squares (MSE): 4.77
Bagging R2-score: 0.84


modelDF = pd.DataFrame({
    'Model'       : ['Random Forest','Bagging',],
    'RMSE'    : [FR_RMSE,BG_RMSE],
    'MAE'   : [FR_MAE,BG_MAE],
    'MSE'   : [FR_MSE,BG_MSE],
    'R2_score'      : [FR_R2_score,BG_R2_score],
    }, columns = ['Model', 'RMSE', 'MAE', 'MSE', 'R2_score'])


for i in modelDF.columns[modelDF.columns!='Model']:
    sns.factorplot(x= 'Model', y= i, data= modelDF, kind='bar',size=2, aspect=2,color='#98FB98',edgecolor='black',linewidth = 1)


sns.histplot(data=y_test, kde=True)

<AxesSubplot:xlabel='interest_rate', ylabel='Count'>


sns.histplot(data=FR_y_prediction,  kde=True)

<AxesSubplot:ylabel='Count'>


sns.histplot(data=BG_y_prediction,  kde=True)

<AxesSubplot:ylabel='Count'>

	emp_title	emp_length	state	homeownership	annual_income	verified_income	debt_to_income	annual_income_joint	verification_income_joint	debt_to_income_joint	delinq_2y	months_since_last_delinq	earliest_credit_line	inquiries_last_12m	total_credit_lines	open_credit_lines	total_credit_limit	total_credit_utilized	num_historical_failed_to_pay	months_since_90d_late	total_collection_amount_ever	current_installment_accounts	accounts_opened_24m	months_since_last_credit_inquiry	num_satisfactory_accounts	num_accounts_120d_past_due	num_active_debit_accounts	total_debit_limit	num_total_cc_accounts	num_open_cc_accounts	num_cc_carrying_balance	num_mort_accounts	account_never_delinq_percent	tax_liens	public_record_bankrupt	loan_purpose	application_type	loan_amount	term	interest_rate	installment	grade	sub_grade	issue_month	loan_status	initial_listing_status	disbursement_method	balance	paid_total	paid_principal	paid_interest
0	global config engineer	3.0	NJ	MORTGAGE	90000.0	Verified	18.01	NaN	NaN	NaN	0	38.0	2001	6	28	10	70795	38767	0	38.0	1250	2	5	5.0	10	0.0	2	11100	14	8	6	1	92.9	0	0	moving	individual	28000	60	14.07	652.53	C	C3	Mar-2018	Current	whole	Cash	27015.86	1999.33	984.14	1015.19
1	warehouse office clerk	10.0	HI	RENT	40000.0	Not Verified	5.04	NaN	NaN	NaN	0	NaN	1996	1	30	14	28800	4321	1	NaN	0	0	11	8.0	14	0.0	3	16500	24	14	4	0	100.0	0	1	debt_consolidation	individual	5000	36	12.61	167.54	C	C1	Feb-2018	Current	whole	Cash	4651.37	499.12	348.63	150.49
2	assembly	3.0	WI	RENT	40000.0	Source Verified	21.15	NaN	NaN	NaN	0	28.0	2006	4	31	10	24193	16000	0	28.0	432	1	13	7.0	10	0.0	3	4300	14	8	6	0	93.5	0	0	other	individual	2000	36	17.09	71.40	D	D1	Feb-2018	Current	fractional	Cash	1824.63	281.80	175.37	106.43
3	customer service	1.0	PA	RENT	30000.0	Not Verified	10.16	NaN	NaN	NaN	0	NaN	2007	0	4	4	25400	4997	1	NaN	0	1	1	15.0	4	0.0	2	19400	3	3	2	0	100.0	1	0	debt_consolidation	individual	21600	36	6.72	664.19	A	A3	Jan-2018	Current	whole	Cash	18853.26	3312.89	2746.74	566.15
4	security supervisor	10.0	CA	RENT	35000.0	Verified	57.96	57000.0	Verified	37.66	0	NaN	2008	7	22	16	69839	52722	0	NaN	0	1	6	4.0	16	0.0	10	32700	20	15	13	0	100.0	0	0	credit_card	joint	23000	36	14.07	786.87	C	C3	Mar-2018	Current	whole	Cash	21430.15	2324.65	1569.85	754.80
5	NaN	NaN	KY	OWN	34000.0	Not Verified	6.46	NaN	NaN	NaN	1	3.0	1990	6	32	12	42100	3898	0	60.0	0	0	2	5.0	12	0.0	1	27200	27	12	5	3	78.1	0	0	other	individual	5000	36	6.72	153.75	A	A3	Jan-2018	Current	whole	Cash	4256.71	873.13	743.29	129.84
6	hr	10.0	MI	MORTGAGE	35000.0	Source Verified	23.66	155000.0	Not Verified	13.12	0	NaN	2004	1	12	10	291852	18916	0	NaN	0	2	1	9.0	10	0.0	3	9100	8	7	6	2	100.0	0	0	credit_card	joint	24000	60	13.59	553.35	C	C2	Jan-2018	Current	whole	Cash	22560.00	2730.51	1440.00	1290.51
7	police	10.0	AZ	MORTGAGE	110000.0	Source Verified	16.19	NaN	NaN	NaN	1	19.0	2005	1	30	15	342336	60805	0	71.0	0	2	4	7.0	15	NaN	5	22250	16	12	10	7	93.0	0	0	debt_consolidation	individual	20000	60	11.99	444.79	B	B5	Jan-2018	Current	whole	Cash	19005.39	1765.84	994.61	771.23
8	parts	10.0	NV	MORTGAGE	65000.0	Source Verified	36.48	NaN	NaN	NaN	1	18.0	1998	3	35	21	283190	69561	0	18.0	0	6	10	4.0	21	0.0	11	36700	19	14	14	2	97.1	0	0	home_improvement	individual	20000	36	13.59	679.58	C	C2	Feb-2018	Current	whole	Cash	18156.66	2703.22	1843.34	859.88
9	4th person	3.0	IL	RENT	30000.0	Not Verified	18.91	NaN	NaN	NaN	0	NaN	2001	0	9	6	33114	17768	0	NaN	0	1	5	17.0	6	0.0	3	14700	7	5	3	0	100.0	0	0	credit_card	individual	6400	36	6.71	196.77	A	A3	Mar-2018	Current	fractional	Cash	6077.13	391.15	322.87	68.28

	emp_length	annual_income	debt_to_income	annual_income_joint	debt_to_income_joint	delinq_2y	months_since_last_delinq	earliest_credit_line	inquiries_last_12m	total_credit_lines	open_credit_lines	total_credit_limit	total_credit_utilized	num_collections_last_12m	num_historical_failed_to_pay	months_since_90d_late	current_accounts_delinq	total_collection_amount_ever	current_installment_accounts	accounts_opened_24m	months_since_last_credit_inquiry	num_satisfactory_accounts	num_accounts_120d_past_due	num_accounts_30d_past_due	num_active_debit_accounts	total_debit_limit	num_total_cc_accounts	num_open_cc_accounts	num_cc_carrying_balance	num_mort_accounts	account_never_delinq_percent	tax_liens	public_record_bankrupt	loan_amount	term	interest_rate	installment	balance	paid_total	paid_principal	paid_interest	paid_late_fees	Id
count	9183.000000	1.000000e+04	9976.000000	1.495000e+03	1495.000000	10000.00000	4342.000000	10000.00000	10000.00000	10000.000000	10000.00000	1.000000e+04	10000.000000	10000.000000	10000.000000	2285.000000	10000.0000	10000.000000	10000.000000	10000.000000	8729.000000	10000.000000	9682.0	10000.0000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.00000
mean	5.930306	7.922215e+04	19.308192	1.279146e+05	19.979304	0.21600	36.760709	2001.29000	1.95820	22.679600	11.40440	1.836062e+05	51049.063100	0.013800	0.167100	46.112473	0.0001	184.334000	2.663600	4.376100	7.340703	11.379600	0.0	0.0001	3.595300	27357.410300	13.030000	8.095000	5.230500	1.383100	94.648990	0.043300	0.123800	16361.922500	43.272000	12.427524	476.205323	14458.916610	2494.234773	1894.448466	599.666781	0.119516	4999.50000
std	3.703734	6.473429e+04	15.004851	7.016838e+04	8.054781	0.68366	21.634939	7.79551	2.38013	11.885439	5.86828	1.876327e+05	53636.731172	0.126535	0.687768	21.748021	0.0100	2206.389859	2.935387	3.158583	5.981590	5.859705	0.0	0.0100	2.434689	26570.164081	7.874713	4.906606	3.327623	1.717274	9.155059	0.593852	0.337172	10301.956759	11.029877	5.001105	294.851627	9964.561865	3958.230365	3884.407175	517.328062	1.813468	2886.89568
min	0.000000	0.000000e+00	0.000000	1.920000e+04	0.320000	0.00000	1.000000	1963.00000	0.00000	2.000000	0.00000	0.000000e+00	0.000000	0.000000	0.000000	2.000000	0.0000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.0000	0.000000	0.000000	2.000000	0.000000	0.000000	0.000000	14.300000	0.000000	0.000000	1000.000000	36.000000	5.310000	30.750000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000
25%	2.000000	4.500000e+04	11.057500	8.683350e+04	14.160000	0.00000	19.000000	1997.00000	0.00000	14.000000	7.00000	5.159375e+04	19185.500000	0.000000	0.000000	29.000000	0.0000	0.000000	1.000000	2.000000	2.000000	7.000000	0.0	0.0000	2.000000	10000.000000	7.000000	5.000000	3.000000	0.000000	92.600000	0.000000	0.000000	8000.000000	36.000000	9.430000	256.040000	6679.065000	928.700000	587.100000	221.757500	0.000000	2499.75000
50%	6.000000	6.500000e+04	17.570000	1.130000e+05	19.720000	0.00000	34.000000	2003.00000	1.00000	21.000000	10.00000	1.146670e+05	36927.000000	0.000000	0.000000	47.000000	0.0000	0.000000	2.000000	4.000000	6.000000	10.000000	0.0	0.0000	3.000000	19500.000000	11.000000	7.000000	5.000000	1.000000	100.000000	0.000000	0.000000	14500.000000	36.000000	11.980000	398.420000	12379.495000	1563.300000	984.990000	446.140000	0.000000	4999.50000
75%	10.000000	9.500000e+04	25.002500	1.515455e+05	25.500000	0.00000	53.000000	2006.00000	3.00000	29.000000	14.00000	2.675500e+05	65421.000000	0.000000	0.000000	63.000000	0.0000	0.000000	3.000000	6.000000	11.000000	14.000000	0.0	0.0000	5.000000	36100.000000	17.000000	10.000000	7.000000	2.000000	100.000000	0.000000	0.000000	24000.000000	60.000000	15.050000	644.690000	20690.182500	2616.005000	1694.555000	825.420000	0.000000	7499.25000
max	10.000000	2.300000e+06	469.090000	1.100000e+06	39.980000	13.00000	118.000000	2015.00000	29.00000	87.000000	51.00000	3.386034e+06	942456.000000	3.000000	52.000000	128.000000	1.0000	199308.000000	35.000000	29.000000	24.000000	51.000000	0.0	1.0000	32.000000	386700.000000	66.000000	46.000000	43.000000	14.000000	100.000000	52.000000	3.000000	40000.000000	60.000000	30.940000	1566.590000	40000.000000	41630.443684	40000.000000	4216.440000	52.980000	9999.00000

	emp_length	annual_income	debt_to_income	annual_income_joint	debt_to_income_joint	months_since_last_delinq	earliest_credit_line	inquiries_last_12m	total_credit_lines	open_credit_lines	total_credit_limit	total_credit_utilized	num_historical_failed_to_pay	months_since_90d_late	total_collection_amount_ever	current_installment_accounts	accounts_opened_24m	months_since_last_credit_inquiry	num_satisfactory_accounts	num_active_debit_accounts	total_debit_limit	num_total_cc_accounts	num_open_cc_accounts	num_cc_carrying_balance	num_mort_accounts	account_never_delinq_percent	tax_liens	public_record_bankrupt	loan_amount	term	interest_rate	installment	balance	paid_total	paid_principal	paid_interest	Id
0	3.0	90000.0	18.01	NaN	NaN	38.0	2001	6	28	10	70795	38767	0	38.0	1250	2	5	5.0	10	2	11100	14	8	6	1	92.9	0	0	28000	60	14.07	652.53	27015.86	1999.33	984.14	1015.19	0
1	10.0	40000.0	5.04	NaN	NaN	NaN	1996	1	30	14	28800	4321	1	NaN	0	0	11	8.0	14	3	16500	24	14	4	0	100.0	0	1	5000	36	12.61	167.54	4651.37	499.12	348.63	150.49	1
2	3.0	40000.0	21.15	NaN	NaN	28.0	2006	4	31	10	24193	16000	0	28.0	432	1	13	7.0	10	3	4300	14	8	6	0	93.5	0	0	2000	36	17.09	71.40	1824.63	281.80	175.37	106.43	2
3	1.0	30000.0	10.16	NaN	NaN	NaN	2007	0	4	4	25400	4997	1	NaN	0	1	1	15.0	4	2	19400	3	3	2	0	100.0	1	0	21600	36	6.72	664.19	18853.26	3312.89	2746.74	566.15	3
4	10.0	35000.0	57.96	57000.0	37.66	NaN	2008	7	22	16	69839	52722	0	NaN	0	1	6	4.0	16	10	32700	20	15	13	0	100.0	0	0	23000	36	14.07	786.87	21430.15	2324.65	1569.85	754.80	4

	emp_title	emp_length	state	homeownership	annual_income	verified_income	debt_to_income	delinq_2y	months_since_last_delinq	earliest_credit_line	inquiries_last_12m	total_credit_lines	open_credit_lines	total_credit_limit	total_credit_utilized	num_historical_failed_to_pay	total_collection_amount_ever	current_installment_accounts	accounts_opened_24m	months_since_last_credit_inquiry	num_satisfactory_accounts	num_active_debit_accounts	total_debit_limit	num_total_cc_accounts	num_open_cc_accounts	num_cc_carrying_balance	num_mort_accounts	account_never_delinq_percent	tax_liens	public_record_bankrupt	loan_purpose	application_type	loan_amount	term	interest_rate	installment	grade	sub_grade	issue_month	loan_status	initial_listing_status	disbursement_method	balance	paid_total	paid_principal	paid_interest	Id
0	global config engineer	3.0	NJ	MORTGAGE	90000.0	Verified	18.01	0	38.0	2001	6	28	10	70795	38767	0	1250	2	5	5.0	10	2	11100	14	8	6	1	92.9	0	0	moving	individual	28000	60	14.07	652.53	C	C3	Mar-2018	Current	whole	Cash	27015.86	1999.33	984.14	1015.19	0
1	warehouse office clerk	10.0	HI	RENT	40000.0	Not Verified	5.04	0	34.0	1996	1	30	14	28800	4321	1	0	0	11	8.0	14	3	16500	24	14	4	0	100.0	0	1	debt_consolidation	individual	5000	36	12.61	167.54	C	C1	Feb-2018	Current	whole	Cash	4651.37	499.12	348.63	150.49	1
2	assembly	3.0	WI	RENT	40000.0	Source Verified	21.15	0	28.0	2006	4	31	10	24193	16000	0	432	1	13	7.0	10	3	4300	14	8	6	0	93.5	0	0	other	individual	2000	36	17.09	71.40	D	D1	Feb-2018	Current	fractional	Cash	1824.63	281.80	175.37	106.43	2
3	customer service	1.0	PA	RENT	30000.0	Not Verified	10.16	0	34.0	2007	0	4	4	25400	4997	1	0	1	1	15.0	4	2	19400	3	3	2	0	100.0	1	0	debt_consolidation	individual	21600	36	6.72	664.19	A	A3	Jan-2018	Current	whole	Cash	18853.26	3312.89	2746.74	566.15	3
4	security supervisor	10.0	CA	RENT	35000.0	Verified	57.96	0	34.0	2008	7	22	16	69839	52722	0	0	1	6	4.0	16	10	32700	20	15	13	0	100.0	0	0	credit_card	joint	23000	36	14.07	786.87	C	C3	Mar-2018	Current	whole	Cash	21430.15	2324.65	1569.85	754.80	4
5	manager	6.0	KY	OWN	34000.0	Not Verified	6.46	1	3.0	1990	6	32	12	42100	3898	0	0	0	2	5.0	12	1	27200	27	12	5	3	78.1	0	0	other	individual	5000	36	6.72	153.75	A	A3	Jan-2018	Current	whole	Cash	4256.71	873.13	743.29	129.84	5
6	hr	10.0	MI	MORTGAGE	35000.0	Source Verified	23.66	0	34.0	2004	1	12	10	291852	18916	0	0	2	1	9.0	10	3	9100	8	7	6	2	100.0	0	0	credit_card	joint	24000	60	13.59	553.35	C	C2	Jan-2018	Current	whole	Cash	22560.00	2730.51	1440.00	1290.51	6
7	police	10.0	AZ	MORTGAGE	110000.0	Source Verified	16.19	1	19.0	2005	1	30	15	342336	60805	0	0	2	4	7.0	15	5	22250	16	12	10	7	93.0	0	0	debt_consolidation	individual	20000	60	11.99	444.79	B	B5	Jan-2018	Current	whole	Cash	19005.39	1765.84	994.61	771.23	7
8	parts	10.0	NV	MORTGAGE	65000.0	Source Verified	36.48	1	18.0	1998	3	35	21	283190	69561	0	0	6	10	4.0	21	11	36700	19	14	14	2	97.1	0	0	home_improvement	individual	20000	36	13.59	679.58	C	C2	Feb-2018	Current	whole	Cash	18156.66	2703.22	1843.34	859.88	8
9	4th person	3.0	IL	RENT	30000.0	Not Verified	18.91	0	34.0	2001	0	9	6	33114	17768	0	0	1	5	17.0	6	3	14700	7	5	3	0	100.0	0	0	credit_card	individual	6400	36	6.71	196.77	A	A3	Mar-2018	Current	fractional	Cash	6077.13	391.15	322.87	68.28	9

	emp_length	annual_income	debt_to_income	months_since_last_delinq	earliest_credit_line	inquiries_last_12m	total_credit_lines	open_credit_lines	total_credit_limit	total_credit_utilized	num_historical_failed_to_pay	total_collection_amount_ever	current_installment_accounts	accounts_opened_24m	months_since_last_credit_inquiry	num_satisfactory_accounts	num_active_debit_accounts	total_debit_limit	num_total_cc_accounts	num_open_cc_accounts	num_cc_carrying_balance	num_mort_accounts	account_never_delinq_percent	tax_liens	public_record_bankrupt	loan_amount	term	interest_rate	installment	balance	paid_total	paid_principal	paid_interest	Id	emp_title_207	emp_title_1035	emp_title_1777	emp_title_3710	emp_title_4675	state_4	state_11	state_30	state_37	state_47	homeownership_2	verified_income_1	verified_income_2	loan_purpose_1	loan_purpose_2	loan_purpose_7	loan_purpose_8	application_type_1	grade_2	grade_3	sub_grade_2	sub_grade_10	sub_grade_12	sub_grade_15	issue_month_1	issue_month_2	loan_status_1	initial_listing_status_1
0	3	1061	1614	37	38	6	26	10	2756	4873	0	587	2	5	5	10	2	132	12	8	6	1	227	0	0	484	1	25	2629	4911	4494	3024	6012	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	1	1	1
1	10	403	371	33	33	1	28	14	638	411	1	0	0	11	8	14	3	200	22	14	4	0	281	0	1	73	0	21	518	856	661	651	1193	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	1	1
2	3	403	1920	27	43	4	29	10	462	1866	0	292	1	13	7	10	3	47	12	8	6	0	232	0	0	21	0	31	130	181	220	163	773	2	1	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	1	0	0	1	0	0	0	1	0	0	1	0
3	1	227	847	33	44	0	2	4	505	489	1	0	1	1	15	4	2	231	1	3	2	0	281	1	0	384	0	6	2671	3889	6130	5204	4303	3	0	1	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	1	1
4	10	316	3568	33	45	7	20	16	2712	6218	0	0	1	6	4	16	10	388	18	15	13	0	281	0	0	409	0	25	2968	4220	5000	4253	5192	4	0	0	0	1	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1	0	0	0	1	0	0	1	1	1

Open credit lines and total credit lines are highly related to each other for both individual and joint application types

The loan amount due to debt_consolidation is highest among other type for the individual application as compared to joint.

Total loan amount for Califonia State is highest and lowest North Dakota.

People with mortgage homes have the highest loan amount

93% of the credit lines are in current status

	Total	Percentage	Dtypes
verification_income_joint	8545	85.45	object
debt_to_income_joint	8505	85.05	float64
annual_income_joint	8505	85.05	float64
months_since_90d_late	7715	77.15	float64
months_since_last_delinq	5658	56.58	float64
months_since_last_credit_inquiry	1271	12.71	float64
emp_title	833	8.33	object
emp_length	817	8.17	float64
num_accounts_120d_past_due	318	3.18	float64
debt_to_income	24	0.24	float64
tax_liens	0	0.00	int64
account_never_delinq_percent	0	0.00	float64
interest_rate	0	0.00	float64
public_record_bankrupt	0	0.00	int64
loan_purpose	0	0.00	object
application_type	0	0.00	object
loan_amount	0	0.00	int64
term	0	0.00	int64
sub_grade	0	0.00	object
installment	0	0.00	float64
grade	0	0.00	object
num_cc_carrying_balance	0	0.00	int64
issue_month	0	0.00	object
loan_status	0	0.00	object
initial_listing_status	0	0.00	object
disbursement_method	0	0.00	object
balance	0	0.00	float64
paid_total	0	0.00	float64
paid_principal	0	0.00	float64
paid_interest	0	0.00	float64
paid_late_fees	0	0.00	float64
num_mort_accounts	0	0.00	int64
num_accounts_30d_past_due	0	0.00	int64
num_open_cc_accounts	0	0.00	int64
total_credit_limit	0	0.00	int64
state	0	0.00	object
homeownership	0	0.00	object
annual_income	0	0.00	float64
verified_income	0	0.00	object
delinq_2y	0	0.00	int64
earliest_credit_line	0	0.00	int64
inquiries_last_12m	0	0.00	int64
total_credit_lines	0	0.00	int64
open_credit_lines	0	0.00	int64
total_credit_utilized	0	0.00	int64
num_total_cc_accounts	0	0.00	int64
num_collections_last_12m	0	0.00	int64
num_historical_failed_to_pay	0	0.00	int64
current_accounts_delinq	0	0.00	int64
total_collection_amount_ever	0	0.00	int64
current_installment_accounts	0	0.00	int64
accounts_opened_24m	0	0.00	int64
num_satisfactory_accounts	0	0.00	int64
num_active_debit_accounts	0	0.00	int64
total_debit_limit	0	0.00	int64
Id	0	0.00	int64

	emp_title	emp_length	state	homeownership	annual_income	verified_income	debt_to_income	months_since_last_delinq	earliest_credit_line	inquiries_last_12m	total_credit_lines	open_credit_lines	total_credit_limit	total_credit_utilized	num_historical_failed_to_pay	total_collection_amount_ever	current_installment_accounts	accounts_opened_24m	months_since_last_credit_inquiry	num_satisfactory_accounts	num_active_debit_accounts	total_debit_limit	num_total_cc_accounts	num_open_cc_accounts	num_cc_carrying_balance	num_mort_accounts	account_never_delinq_percent	tax_liens	public_record_bankrupt	loan_purpose	application_type	loan_amount	term	interest_rate	installment	grade	sub_grade	issue_month	loan_status	initial_listing_status	balance	paid_total	paid_principal	paid_interest	Id
0	1777	3	30	0	1061	2	1614	37	38	6	26	10	2756	4873	0	587	2	5	5	10	2	132	12	8	6	1	227	0	0	7	0	484	1	25	2629	2	12	2	1	1	4911	4494	3024	6012	0
1	4675	10	11	2	403	0	371	33	33	1	28	14	638	411	1	0	0	11	8	14	3	200	22	14	4	0	281	0	1	2	0	73	0	21	518	2	10	0	1	1	856	661	651	1193	1
2	207	3	47	2	403	1	1920	27	43	4	29	10	462	1866	0	292	1	13	7	10	3	47	12	8	6	0	232	0	0	8	0	21	0	31	130	3	15	0	1	0	181	220	163	773	2
3	1035	1	37	2	227	0	847	33	44	0	2	4	505	489	1	0	1	1	15	4	2	231	1	3	2	0	281	1	0	2	0	384	0	6	2671	0	2	1	1	1	3889	6130	5204	4303	3
4	3710	10	4	2	316	2	3568	33	45	7	20	16	2712	6218	0	0	1	6	4	16	10	388	18	15	13	0	281	0	0	1	1	409	0	25	2968	2	12	2	1	1	4220	5000	4253	5192	4